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024 7 _a10.1007/978-3-030-67681-0
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050 4 _aQA76.76.E95
050 4 _aQ387-387.5
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072 7 _aCOM025000
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072 7 _aUYQE
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082 0 4 _a006.33
_223
245 1 0 _aProvenance in Data Science
_h[electronic resource] :
_bFrom Data Models to Context-Aware Knowledge Graphs /
_cedited by Leslie F. Sikos, Oshani W. Seneviratne, Deborah L. McGuinness.
250 _a1st ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _aXI, 110 p. 24 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aAdvanced Information and Knowledge Processing,
_x2197-8441
505 0 _aThe Evolution of Context-Aware RDF Knowledge Graphs -- Data Provenance and Accountability on the Web -- The Right (Provenance) Hammer for the Job: a Comparison of Data Provenance Instrumentation -- Contextualized Knowledge Graphs in Communication Network and Cyber-Physical System Modeling -- ProvCaRe: A Large-Scale Semantic Provenance Resource for Scientific Reproducibility -- Graph-Based Natural Language Processing for the Pharmaceutical Industry.
520 _aRDF-based knowledge graphs require additional formalisms to be fully context-aware, which is presented in this book. This book also provides a collection of provenance techniques and state-of-the-art metadata-enhanced, provenance-aware, knowledge graph-based representations across multiple application domains, in order to demonstrate how to combine graph-based data models and provenance representations. This is important to make statements authoritative, verifiable, and reproducible, such as in biomedical, pharmaceutical, and cybersecurity applications, where the data source and generator can be just as important as the data itself. Capturing provenance is critical to ensure sound experimental results and rigorously designed research studies for patient and drug safety, pathology reports, and medical evidence generation. Similarly, provenance is needed for cyberthreat intelligence dashboards and attackmaps that aggregate and/or fuse heterogeneous data from disparate data sources to differentiate between unimportant online events and dangerous cyberattacks, which is demonstrated in this book. Without provenance, data reliability and trustworthiness might be limited, causing data reuse, trust, reproducibility and accountability issues. This book primarily targets researchers who utilize knowledge graphs in their methods and approaches (this includes researchers from a variety of domains, such as cybersecurity, eHealth, data science, Semantic Web, etc.). This book collects core facts for the state of the art in provenance approaches and techniques, complemented by a critical review of existing approaches. New research directions are also provided that combine data science and knowledge graphs, for an increasingly important research topic.
650 0 _aExpert systems (Computer science).
650 0 _aData mining.
650 0 _aData structures (Computer science).
650 0 _aInformation theory.
650 0 _aMachine learning.
650 1 4 _aKnowledge Based Systems.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aData Structures and Information Theory.
650 2 4 _aMachine Learning.
700 1 _aSikos, Leslie F.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aSeneviratne, Oshani W.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aMcGuinness, Deborah L.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030676803
776 0 8 _iPrinted edition:
_z9783030676827
776 0 8 _iPrinted edition:
_z9783030676834
830 0 _aAdvanced Information and Knowledge Processing,
_x2197-8441
856 4 0 _uhttps://doi.org/10.1007/978-3-030-67681-0
912 _aZDB-2-SCS
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